A Look at Machine Learning for Data Entry Automation

At its most basic, data entry tends to be extremely repetitive and rote. There are different forms of data entry, of course, but they all have the same goal: to transcribe an existing document or section of data to a more convenient digital format.

There are professionals who specifically handle data entry tasks for a company or organization, but there is some aspect of it in almost every modern position. A receptionist for a doctor’s office, for example, might have to handle the data entry for patient information and scheduling. A business analyst might have to transcribe or modify existing performance data to be used with software tools.

But there are some major problems with requiring everyone to participate in a data entry process, most of which relate to the veracity and security of the resulting information. Transcription and transposition errors are the most common entry issues, and entry mistakes alone can be quite costly.

Furthermore, these errors make it harder for true data entry and data analyst professionals who must painstakingly assess the accuracy of incoming information.

The good news, however, is that every data entry operation stands to benefit from the adoption of machine learning and robotic process automation (RPA). After deployment, life is so much easier for the professionals behind the scenes. To understand why, we have to dig a little deeper.

What Is Robotic Process Automation and How Does Machine Learning Help?

Robotic process automation is a relatively new form of business process automation that involves creating a repeatable operation that has little oversight and minor human input. RPA specifically allows companies to automate repetitive or programmable tasks so that employees no longer have to do them, and as an added bonus, they can shift their focus elsewhere.

Generally, there are no changes to the existing process, interface or systems outside of making sure everything is compatible with the automation platforms.

Machine learning and AI come into the equation because they can be used to systematically improve the entire operation, by learning more efficient and more accurate traits over time. Think of it like the cognitive human brain that learns more and more through experience.

These technologies, when used together, can help automate laborious and repetitive work, freeing up workers for more important tasks. Automation systems themselves never tire or grow weary, never get burnt out, and always follow their programmed responses, which means they tend to be much more accurate and effective than their human counterparts.

RPA can be used to take over repetitive tasks that employees would carry out about 50 to 60 times per day, freeing up a lot of time. It can also be used for mass data entry or document generation like mass emails. It can analyze and process lists and other data. Really, we could go on for days.

Beyond data entry, so-called “knowledge workers” also benefit from RPA. As Harvard Business Review explains, “Knowledge workers consistently tell us they want to be liberated from such highly structured, routine, and dreary tasks to focus on more interesting work. Some are actually getting that wish, thanks to a new approach known as Robotic Process Automation (RPA).”

And for data entry particularly, RPA and machine learning can help cut down on errors, improve performance, and boost security. Fewer people have access to sensitive data, especially when an automated system can convert, move or translate information without anyone else ever touching it — this is better for security, obviously.

With RPA, cost savings, accuracy and efficiency all improve thanks to the elimination of entry errors.

How to Incorporate RPA and Machine Learning

The benefits sound great, so how do you get started?

Unfortunately, it’s a bit more complicated than simply making the switch as soon as you want to. You’ll first need to assess which processes and operations can be automated with the technology. Then, you’ll need to train your workforce to operate alongside such tools, including data entry professionals.

Finally, you’ll need to develop an infrastructure that supports their use and provides the necessary access to all incoming data channels, sources and platforms.

It’s not an easy task, but it’s one that’s well worth the investments.